






"""simple docstring"""


import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						lowerCAmelCase   :Tuple						=			filter(lambda a__ : p.requires_grad     ,      model.parameters() )
						lowerCAmelCase   :Optional[Any]						=			sum([np.prod(p.size() ) for p in model_parameters] )
						return params


__SCREAMING_SNAKE_CASE   =   logging.getLogger(__name__)


def        UpperCAmelCase      (					a__     ,      a__ ):





						'''simple docstring'''






						if metric == "rouge2":
												lowerCAmelCase   :int						=			'{val_avg_rouge2:.4f}-{step_count}'
						elif metric == "bleu":
												lowerCAmelCase   :Optional[Any]						=			'{val_avg_bleu:.4f}-{step_count}'
						elif metric == "em":
												lowerCAmelCase   :Any						=			'{val_avg_em:.4f}-{step_count}'
						else:
												raise NotImplementedError(
												    F"""seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this"""
												    ' function.' )

						lowerCAmelCase   :Any						=			ModelCheckpoint(
						    dirpath=a__     ,      filename=a__     ,      monitor=F"""val_{metric}"""     ,      mode='max'     ,      save_top_k=3     ,      every_n_epochs=1     ,      )
						return checkpoint_callback


def        UpperCAmelCase      (					a__     ,      a__ ):





						'''simple docstring'''






						return EarlyStopping(
						    monitor=F"""val_{metric}"""     ,      mode='min' if 'loss' in metric else 'max'     ,      patience=a__     ,      verbose=a__     ,      )







class 						__UpperCamelCase     (     pl.Callback						):
					def        UpperCAmelCase__ (							self							:	Union[str, Any]     , UpperCAmelCase							:	Optional[int]     , UpperCAmelCase							:	str   )	->       List[str]:
											lowerCAmelCase   :Optional[int]						=			{f"""lr_group_{i}""": param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups   )}
											pl_module.logger.log_metrics(UpperCAmelCase   )
					@rank_zero_only
					def        UpperCAmelCase__ (							self							:	Union[str, Any]     , UpperCAmelCase							:	pl.Trainer     , UpperCAmelCase							:	pl.LightningModule     , UpperCAmelCase							:	str     , UpperCAmelCase							:	str=True   )	->       None:
											logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****"""   )
											lowerCAmelCase   :str						=			trainer.callback_metrics
											trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['log', 'progress_bar', 'preds']}   )
											# Log results
											lowerCAmelCase   :List[str]						=			Path(pl_module.hparams.output_dir   )
											if type_path == "test":
																	lowerCAmelCase   :int						=			od / 'test_results.txt'
																	lowerCAmelCase   :Any						=			od / 'test_generations.txt'
											else:
																	# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
																	# If people want this it will be easy enough to add back.
																	lowerCAmelCase   :Tuple						=			od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
																	lowerCAmelCase   :Optional[Any]						=			od / f"""{type_path}_generations/{trainer.global_step:05d}.txt"""
																	results_file.parent.mkdir(exist_ok=UpperCAmelCase   )
																	generations_file.parent.mkdir(exist_ok=UpperCAmelCase   )
											with open(UpperCAmelCase     , 'a+'   ) as writer:
																	for key in sorted(UpperCAmelCase   ):
																							if key in ["log", "progress_bar", "preds"]:
																													continue
																							lowerCAmelCase   :str						=			metrics[key]
																							if isinstance(UpperCAmelCase     , torch.Tensor   ):
																													lowerCAmelCase   :List[Any]						=			val.item()
																							lowerCAmelCase   :Any						=			f"""{key}: {val:.6f}\n"""
																							writer.write(UpperCAmelCase   )

											if not save_generations:
																	return

											if "preds" in metrics:
																	lowerCAmelCase   :List[Any]						=			'\n'.join(metrics['preds']   )
																	generations_file.open('w+'   ).write(UpperCAmelCase   )
					@rank_zero_only
					def        UpperCAmelCase__ (							self							:	Dict     , UpperCAmelCase							:	Dict     , UpperCAmelCase							:	Dict   )	->       int:
											try:
																	lowerCAmelCase   :Tuple						=			pl_module.model.model.num_parameters()
											except AttributeError:
																	lowerCAmelCase   :List[Any]						=			pl_module.model.num_parameters()

											lowerCAmelCase   :List[str]						=			count_trainable_parameters(UpperCAmelCase   )
											# mp stands for million parameters
											trainer.logger.log_metrics({'n_params': npars, 'mp': npars / 1e6, 'grad_mp': n_trainable_pars / 1e6}   )
					@rank_zero_only
					def        UpperCAmelCase__ (							self							:	Any     , UpperCAmelCase							:	pl.Trainer     , UpperCAmelCase							:	pl.LightningModule   )	->       int:
											save_json(pl_module.metrics     , pl_module.metrics_save_path   )
											return self._write_logs(UpperCAmelCase     , UpperCAmelCase     , 'test'   )
					@rank_zero_only
					def        UpperCAmelCase__ (							self							:	int     , UpperCAmelCase							:	pl.Trainer     , UpperCAmelCase							:	Optional[Any]   )	->       Dict:
											save_json(pl_module.metrics     , pl_module.metrics_save_path   )
											# Uncommenting this will save val generations
											# return self._write_logs(trainer, pl_module, "valid")







"""simple docstring"""


from math import sqrt


def        UpperCAmelCase      (					a__ = 1_00_00_00 ):





						'''simple docstring'''






						lowerCAmelCase   :int						=			0
						lowerCAmelCase   :int						=			0
						lowerCAmelCase   :int

						while num_cuboids <= limit:
												max_cuboid_size += 1
												for sum_shortest_sides in range(2     ,      2 * max_cuboid_size + 1 ):
																		if sqrt(sum_shortest_sides**2 + max_cuboid_size**2 ).is_integer():
																								num_cuboids += (
																								    min(a__     ,      sum_shortest_sides // 2 )
																								    - max(1     ,      sum_shortest_sides - max_cuboid_size )
																								    + 1
																								)

						return max_cuboid_size


if __name__ == "__main__":
		print(F"""{solution() = }""")